In large-scale e-commerce platforms, Conversion Rate (CVR) prediction is crucial for recommender system, yet existing approach face a fundamental granularity mismatch: models operate at the item level while users purchase at the fine-grained Stock Keeping Unit (SKU) level. This mismatch causes loss of fine-grained user intent signals. Moreover, it also introduces price inconsistency bias due to the gap between static exposure prices and actual transaction prices. While direct SKU-level modeling would resolve these issues, it is impractical for industrial deployment due to the extreme data sparsity and prohibitive inference costs.

Integrated sensing and communication (ISAC) has been seen as a promising technology to satisfy the dual requirements of communication and sensing for the emerging applications in the next-generation wireless networks. In this paper, we research one down-link multi-cell orthogonal frequency division multiple access (OFDMA) ISAC system, in which a group of collaborative ISAC base stations send signals to their corresponding communication users, and concurrently work with multiple sensing receivers to estimate locations of multiple targets. Specifically, we investigate the joint sub-channel assignment and power allocation for users and targets to maximize the sum-rate, while ensuring the minimal signal-to-interference-plus-noise ratio (SINR) constraint for each user and the maximal Cramer-Rao lower bound (CRLB) requirement for each target. We propose a deep reinforcement learning (DRL) approach to address the above sub-channel assignment and power allocation problems. In our approach, we adopt the dueling deep Q network (DDQN) and the deep deterministic policy gradient (DDPG) network to output the sub-channel assignment policy and power allocation policy separately. Simulation results aim to prove the effectiveness of our proposed algorithm.

The technology of specific target detection and recognition in Synthetic Aperture Radar (SAR) image is one of the most important issues especially in the area of remote sensing observation. Aiming at ship detection in complex scenario, this paper presents a universal framework and a novel method called Speed-up Seed point OTSU (SSOTSU) based on OTSU and invoked in the framework. Different from the classic OTSU method, SSOTSU obtains seed points and compute threshold in specific area around every seed point. The experiment results demonstrate that our method can be implemented to detect ships under complicated scenario of both SAR images and optical images fast and obtain high accuracy in detection.

For online multi-object tracking, the appearance model of a target is essential. It has to be consistent within the track of a target and be discriminative between tracks of different targets. To satisfy these requirements, a new two-stage frame-by-frame method that takes advantage of both global and local features is proposed. In this paper, first, targets are tracked with their global feature that is more consistent than local features. Then, a discriminative local feature-MSER (maximally stable extremal regions)-based color histogram is proposed and used for targets tracking. Experiments on several public datasets shows improvement in performance over other state-of-the-art methods.